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ARXIV:2603.14717 · PROTEIN GENERATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE
ARXIV:2603.14717PROTEIN GENERATIONSUBMITTED 02 APR · 02:30 UTCFRESHNESS STALEarXiv
A training-free method for generating protein sequences from small family alignments using stochastic attention.
Opportunity summary
Pain A training-free method for generating protein sequences from small family alignments using stochastic attention.
Evidence 0 refs | 0 sources | 17% coverage
Blocker Evidence unverified
A training-free method for generating protein sequences from small family alignments using stochastic attention. We propose stochastic attention (SA), a training-free sampler that treats the modern Hopfield energy over a protein alignment as a…
Most protein families have fewer than 100 known members, a regime where deep generative models overfit or collapse. We propose stochastic attention (SA), a training-free sampler that treats the modern Hopfield energy over a…
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Controls confirm SA encodes correlated substitution patterns, not just per-position amino acid frequencies.
Protein Generation moved forward this cycle; last verified April 2026. Public score 3.0/10.
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A training-free method for generating protein sequences from small family alignments using stochastic attention.
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10.48550/arXiv.2603.14717A training-free method for generating protein sequences from small family alignments using stochastic attention.
Abstract
Most protein families have fewer than 100 known members, a regime where deep generative models overfit or collapse. We propose stochastic attention (SA), a training-free sampler that treats the modern Hopfield energy over a protein alignment as a Boltzmann distribution and draws samples via Langevin dynamics. The score function is a closed-form softmax attention operation requiring no training, no pretraining data, and no GPU, with cost linear in alignment size. Across eight Pfam families, SA generates sequences with low amino acid compositional divergence, substantial novelty, and structural plausibility confirmed by ESMFold and AlphaFold2. Generated sequences fold more faithfully to canonical family structures than natural members in six of eight families. Against profile HMMs, EvoDiff, and the MSA Transformer, which produce sequences that drift far outside the family, SA maintains 51 to 66 percent identity while remaining novel, in seconds on a laptop. The critical temperature governing generation is predicted from PCA dimensionality alone, enabling fully automatic operation. Controls confirm SA encodes correlated substitution patterns, not just per-position amino acid frequencies.
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PROBLEM
A training-free method for generating protein sequences from small family alignments using stochastic attention. We propose stochastic attention (SA), a training-free sampler that treats the modern Hopfield energy over a protein alignment as a Boltzmann distribution and draws sa...
METHOD
Most protein families have fewer than 100 known members, a regime where deep generative models overfit or collapse. We propose stochastic attention (SA), a training-free sampler that treats the modern Hopfield energy over a protein alignment as a Boltzmann distribution and draws...
RESULT
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Controls confirm SA encodes correlated substitution patterns, not just per-position amino acid frequencies.
WHY NOW
Protein Generation moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed public claims while anchored extraction refreshes.
A training-free method for generating protein sequences from small family alignments using stochastic attention. We propose stochastic attention (SA), a training-free sampler that treats the modern Hopfield energy over a protein alignment as a Boltzmann distribution and draws samples via Langevin dynamics.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Most protein families have fewer than 100 known members, a regime where deep generative models overfit or collapse. We propose stochastic attention (SA), a training-free sampler that treats the modern Hopfield energy over a protein alignment as a Boltzmann distribution and draws samples via Langevin dynamics.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 3.0/10 on the public viability pass. Controls confirm SA encodes correlated substitution patterns, not just per-position amino acid frequencies.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Protein Generation moved forward this cycle; last verified April 2026. Public score 3.0/10.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A training-free method for generating protein sequences from small family alignments using stochastic attention.
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Protein Generation
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3.0/10 public viability
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